Ophthalmic data & Databse
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This document describes an ophthalmic data and database that provides several benefits: - Validated clinical data from scientific studies and screenings that can reduce analysis time and costs while improving data quality. - Easy electronic access to safety, efficacy, and predictability results that can help grow a practice and create electronic patient folders. - The database uses a low-cost and easy-to-use Microsoft software interface.
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1Big Data Analytics forHealthcareChandan K. ReddyD.docx
1
Big Data Analytics for
Healthcare
Chandan K. Reddy
Department of Computer Science
Wayne State University
Jimeng Sun
Healthcare Analytics Department
IBM TJ Watson Research Center
2Jimeng Sun, Large-scale Healthcare Analytics
Healthcare Analytics using Electronic Health Records (EHR)
Old way: Data are expensive and small
– Input data are from clinical trials, which is small
and costly
– Modeling effort is small since the data is limited
• A single model can still take months
EHR era: Data are cheap and large
– Broader patient population
– Noisy data
– Heterogeneous data
– Diverse scale
– Complex use cases
3Jimeng Sun, Large-scale Healthcare Analytics
Heterogeneous Medical Data
DiagnosisDiagnosis
MedicationMedication
LabLab
Clinical
notes
Clinical
notes
ImagesImages
Genetic
data
Genetic
data
4Jimeng Sun, Large-scale Healthcare Analytics
Challenges of Healthcare AnalyticsScalability ChallengesChallenges in Healthcare Analytics
Collaboration across domains
Analytic platform
Intuitive results
Scalable computation
5
PARALLEL MODEL BUILDING
6Jimeng Sun, Large-scale Healthcare Analytics
Motivation – Predictive modeling using EHR is growing
Need for scalable predictive modeling platforms/systems due to increased
computational requirements from:
– Processing EHR data (due to volume, variability, and heterogeneity)
– Building accurate models
– Building clinically meaningful models
– Validating models for accuracy and generalizability
Explosion in
interest
7Jimeng Sun, Large-scale Healthcare Analytics
What does it take to develop a predictive model using EHR?
Marina: IBM
Analytics Consultant
1
2
3
4
5
Within 3 months, we need to
1. understand business case
2. obtain the data
3. prepare the data
4. develop predictive models
5. deliver the final model
David Gotz, Harry Starvropoulos, Jimeng Sun, Fei Wang.
ICDA: A Platform for Intelligent Care Delivery Analytics, AMIA 2012
8Jimeng Sun, Large-scale Healthcare Analytics
A Generalized Predictive Modeling Pipeline
Cohort Construction: Find an appropriate set of patients with the specified
target condition and a corresponding set of control patients without the
condition.
Feature Construction: Compute a feature vector representation for each
patient based on the patient’s EHR data.
Cross Validation: Partition the data into complementary subsets for use in
model training and validation testing.
Feature Selection: Rank the input features and select a subset of relevant
features for use in the model.
Classification: The training and evaluation of a model for a specific classifier.
Output: Clean up intermediate files and to put results into their final locations.
Model specification
9Jimeng Sun, Large-scale Healthcare Analytics
Cohort Construction
A
ll
pa
tie
nt
s
D1
Disease Target samples
D1 Hypertension control 5000
D2 Heart failure onset 33K
D3 Hypertension diagnosis 300K
Cases
Controls
D3
D2
10Jimeng Sun, Large- ...
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Selecting higher risk cohorts for screening results in a more effective and cost-effective test. Screening focuses on intermediate risk individuals and requires selective screening using tests that are optimally clinically effective and have added value in accurately detecting high risk individuals while excluding treatment for low risk individuals. Such screening uses low cost tests that can be widely utilized.
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Selected Abstracts & Articles
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Valuable information for orthopedic and neurosurgeons
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Νέα σειρά φωτισμού ασφαλείας για CCTV από τη GNEMS
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- 14. 1. 2. 3. 4. 5. 6. 7. 8. Alpins N. Analysis of aggregate surgically induced refractive change, prediction error, and intraocular astigmatism. J Refract Surg. 2001 Nov-Dec;17(6):705-7. Naeser K, Hjortdal J. Polar value analysis of refractive data. J Cataract Refract Surg. 2001 Jan;27(1):86-94. Holladay JT, Moran JR, Kezirian GM. Analysis of aggregate surgically induced refractive change, prediction error, and intraocular astigmatism. J Cataract Refract Surg. 2001 Jan;27(1):61-79. Alpins N. Astigmatism analysis by the Alpins method. J Cataract Refract Surg. 2001 Jan;27(1):31-49. Kaye SB, Harris WF. Analyzing refractive data. J Cataract Refract Surg. 2002 Dec;28(12):210916. Naeser K, Hjortdal J. Multivariate analysis of refractive data: mathematics and statistics of spherocylinders. J Cataract Refract Surg. 2001 Jan;27(1):129-42. Harris WF. Analysis of astigmatism in anterior segment surgery. J Cataract Refract Surg. 2001 Jan;27(1):107-28. Thibos LN, Wheeler W, Horner D. Power vectors: an application of Fourier analysis to the description and statistical analysis of refractive error. Optom Vis Sci. 1997 Jun;74(6):367-75. Review. Graphic reporting of outcomes of refractive surgery. Reinstein DZ, Waring GO 3rd. J Refract Surg. 2009 Nov;25(11):975-8.